Papers by Seokhee Hong
Who Wrote this Code? Watermarking for Code Generation (2024.acl-long)
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| Challenge: | Existing methods to detect machine-generated text by embedding watermarks fail to function appropriately in code generation tasks due to the task’s nature of having low entropy. |
| Approach: | They propose a logit-modifying watermark method which enhances detection ability and mitigates code quality degeneration by removing low-entropy segments at generating and detecting watermarks. |
| Outcome: | The proposed method outperforms baseline methods in detecting machine-generated code text while preserving code quality. |
KoSBI: A Dataset for Mitigating Social Bias Risks Towards Safer Large Language Model Applications (2023.acl-industry)
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| Challenge: | Existing research and resources are not readily applicable in South Korea due to the differences in language and culture, both of which significantly affect the biases and targeted demographic groups. |
| Approach: | They propose a social bias dataset of 34k pairs of contexts and sentences in Korean covering 72 demographic groups in 15 categories. |
| Outcome: | The proposed dataset reduces social biases by 16.47%p on average for HyperClova (30B and 82B), and GPT-3. |
How Robust are Fact Checking Systems on Colloquial Claims? (2021.naacl-main)
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| Challenge: | Existing fact checking systems that perform well on colloquial claims significantly degenerate on collotic claims with the same semantics. |
| Approach: | They propose to transfer the styles of claims from FEVER into colloquialism to investigate fact checking systems on colloqual claims. |
| Outcome: | The proposed system significantly degenerates on colloquial claims with the same semantics. |
MANTA: A Scalable Pipeline for Transmuting Massive Web Corpora into Instruction Datasets (2025.findings-emnlp)
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| Challenge: | MANTA-1M generates high-quality large-scale instruction fine-tuning datasets from web corpora . scalability and diversity of the datasets are preserved, allowing expansion into domains requiring intensive knowledge. |
| Approach: | a team of researchers introduce a pipeline that fine-tunes large-scale instruction datasets from web corpora with minimal human intervention. |
| Outcome: | MANTA generates high-quality large-scale instruction fine-tuning datasets from web corpora . leveraging high-performance LLMs, MANTE outperforms other methods in knowledge-intensive tasks . |
From KMMLU-Redux to Pro: A Professional Korean Benchmark Suite for LLM Evaluation (2025.findings-emnlp)
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| Challenge: | Using Korean expert-level benchmarks, Large Language Models can be developed in real-world scenarios. |
| Approach: | They introduce two Korean expert-level benchmarks that reflect professional knowledge in Korea. |
| Outcome: | The proposed benchmarks represent professional knowledge in Korea. |
SQuARe: A Large-Scale Dataset of Sensitive Questions and Acceptable Responses Created through Human-Machine Collaboration (2023.acl-long)
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Hwaran Lee, Seokhee Hong, Joonsuk Park, Takyoung Kim, Meeyoung Cha, Yejin Choi, Byoungpil Kim, Gunhee Kim, Eun-Ju Lee, Yong Lim, Alice Oh, Sangchul Park, Jung-Woo Ha
| Challenge: | Existing studies focus on coping with social harms that large language models pose . however, discussions on sensitive issues can become toxic even if the users are well-intentioned. |
| Approach: | They propose to use Korean dataset to test whether LLMs can generate offensive content and propagate prejudices. |
| Outcome: | The proposed dataset shows that acceptable response generation improves for HyperCLOVA and GPT-3. |